Master the key concepts of Hadoop and Big data. Join and learn from a Big Data/Hadoop expert.

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Big Data & Hadoop


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  • Big Data Hadoop is one of the most popular open-source utility software platforms that help store a huge amount of data and also has a massive processing power in it. It can multitask, i.e., it can handle unlimited data simultaneously.
  • Things you will learn:
    • In-depth knowledge of Big Data Hadoop features like HDFS, YARN, Hive, Map Reduce, Pig, Spark.

      Get the opportunity to work on real-life industry cases.

      Master the desired skill with or without any technical background.

      Big Data Hadoop is the most in-demand skills at present.

      Popular Big Data job titles Developer, Administrator, Data Analyst, Tester, and Solution Architect.

  • If you want to enhance your career potential, then Big Data Hadoop could be an ideal choice. so, what are you waiting for Simply enrol for the best Big Data Hadoop course without breaking your bank.

Big Data Hadoop Training


  • With our Big Data Hadoop training and certification program online, you are likely to learn a multitude of skills and implement them at work. The main objective of our training program is:
    • Learn YARN, Map Reduce, and become an expert in writing apps using the mentioned tools.

      To impart an in-depth knowledge of HDFS, Sqoop, Pig, Hive, Oozie, shell scripting, Spark, Flume, Zookeeper online.

      You will understand Big Data Hadoop cluster and learn Big Data Hadoop analytics as well.

      Configure various ETL tools and know-how to set the pseudo nodes.

      Work on real-life projects and hands-on assignments to facilitate the learning process.

  • According to the recent survey conducted, the estimated salary of a Big Data Hadoop Developer ranges between $119,250 to $168,250 per year. when you choose us for your Big Data Hadoop training, you are likely to get higher salary packages, unlike other IT departments.
    • Big Data Analytics is proven to be the most preferred software or data analytic tool.

      These tools are considered to increase the efficiency of any organization.

      Organizations use Big Data Analytic tools to get better insights into their sales.

      Big data is also helping businesses to promote marketing on social media to meet their goals.

      There is an acute shortage of skilled analysts and managers in the coming years.

  • So, acknowledge the coming opportunity, focus on sharpening the required skills and get ready to grasp the chance when it comes.

  • Our Big Data Hadoop training is backed by accredited experts along with the facility of real-time sessions. After the completion of your course, you will get significant career growth;
    • Our Big Data Hadoop training lessons include the perfect combination of theoretical as well as practical components to make the training conducive for learning.

      After completing the basic level Big Data Hadoop training, learners are qualified for the next level and thus are prepared for it.

      In Big Data Hadoop training, individuals are provided with the basic concepts to recap and sharpen their skills.

      Get Big Data Hadoop Live sessions by providing recordings of the live class, study material, ppt, projects, etc.

      Having proper training and the right certification can reap you a perfect result.

  • Big Data Hadoop is one of the first frameworks for extracting and transforming huge volumes of data and hence is widely used making it the most popular big data framework. The best part of the Big Data Hadoop system is its flexibility to control the overall system and store data in HDFS and supports data compression. Data can be installed using tools such as Map Reduce, Pig, and Hive.
    • There is a great demand for Big Data Managers and it will increase with time.

      It is predicted by 2022, the Big Data Hadoop market will reach $99.31.

      In the US, there is a shortage of Big Data Hadoop skilled experts up to 1.6 million.

      Almost every top MNCs are adopting the Big Data Hadoop system.

      Get the most outstanding learning experience from Coma Campus.

  • Here are some job roles and responsibilities covered as a part of the Big Data Hadoop training program:
    • You should know how to develop, implement and maintain Big Data solutions.

      You should conduct database modelling and development, data mining, and warehousing.

      You must be well-versed in designing and deploying data platforms.

      You should know to improve data efficiency, reliability & quality, create data enrichment, build high performance, etc.

      You must have substantial knowledge in MongoDB, RDBMS, Spark core, HBase or Cassandra, Pig, Yarn, SQL, etc.

      You must take a pivotal part in the development and deployment of innovative big data platforms.

  • One of the remarkable flairs of Big Data Hadoop is the ease with which it can be scaled up. The capability of processing a large amount of data in very little time makes it even more appealing.
    • You can clear any certification exam successfully.

      You will be offered a certificate that is accepted worldwide.

      Take up a lucrative and promising career ahead.

      Increase your worth in today’s tech-driven landscape.

      Our certification will let you bag higher salary packages.

  • Many top MNCs are on the lookout for professional Big Data and Analytics for their businesses.

  • Training is vital to future proof your employability but getting the right training will cement your position. Our Big Data Hadoop certification training may help you learn Data Hadoop concepts without any difficulty.
    • Help you clear any certification exam successfully.

      After the course, you will be offered a certificate thereafter.

      You can establish yourself as a certified Developer, Administrator, Data Analyst, Tester, or Solution Architect.

      Register now and ramp up your career and own it.

Why Should You Learn Big Data Hadoop?

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Plenary for Big Data Hadoop Training

Track Week Days Weekends Fast Track
Course Duration 40-45 Days 7 Weekends 8 Days
Hours 1 Hrs. Per Day 2 Hrs. Per Day 6+ Hrs. Per Day
Training Mode Classroom/Online Classroom/Online Classroom/Online
Course Price at :
10% OFF Expires in01D 08H 53M 23S

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Big Data Hadoop Training

  • Croma Campus offers the best Hadoop development Training in Noida with most experienced professionals. Our Instructors are working in Big Data Space and related technologies for years in MNC's.
  • We aware of industry needs and we are offering Hadoop development Training in more practical way. Our team of Hadoop trainers offers the in-classroom training with best industry practices.
  • We framed our syllabus to match with the real-world requirements for beginner level to advanced level. Our training will be handled in either weekday or weekends programme depends on participants requirements.
  • In this program you will learn:
    • Introduction to Big Data & Hadoop



      Managing and Scheduling Jobs

      Apache Sqoop

      Apache Flume

      Getting Data into HDFS

      Apache Kafka

      Hadoop Clients

      Cluster Maintenance

      Cloudera Manager

      Cluster Monitoring and Troubleshooting

      Planning Your Hadoop Cluster

      Advanced Cluster Configuration

      MapReduce Framework

      Apache PIG

      Apache HIVE

      No SQL Databases HBase

      Functional Programming using Scala

      Apache Spark

      Hadoop Datawarehouse

      Writing MapReduce Program

      Introduction to Combiner

      Problem-solving with MapReduce

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  • Introduction to Big Data
    • Overview of Course

      What is Big Data

      Big Data Analytics

      Challenges of Traditional System

      Distributed Systems

  • Introduction to Hadoop
    • Components of Hadoop Ecosystem

      Commercial Hadoop Distributions

      Why Hadoop

      Fundamental Concepts in Hadoop

  • Security in Hadoop
    • Why Hadoop Security Is Important

      Hadoop’s Security System Concepts

      What Kerberos Is and How it Works

      Securing a Hadoop Cluster with Kerberos

  • Initial Setup and Configuration
    • Deployment Types

      Installing Hadoop

      Specifying the Hadoop Configuration

      Performing Initial HDFS Configuration

      Performing Initial YARN and MapReduce Configuration

      Hadoop Logging

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  • HDFS
    • What is HDFS

      Need for HDFS

      Regular File System vs HDFS

      Characteristics of HDFS

      HDFS Architecture and Components

      High Availability Cluster Implementations

      HDFS Component File System Namespace

      Data Block Split

      Data Replication Topology

      HDFS Command Line

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  • YARN
    • Yarn Introduction

      Yarn Use Case

      Yarn and its Architecture

      Resource Manager

      How Resource Manager Operates

      Application Master

      How Yarn Runs an Application

      Tools for Yarn Developers

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  • Managing and Scheduling Jobs
    • Managing Running Jobs

      Scheduling Hadoop Jobs

      Configuring the Fair Scheduler

      Impala Query Scheduling

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  • Apache Sqoop
    • Apache Sqoop

      Sqoop and Its Uses

      Sqoop Processing

      Sqoop Import Process

      Sqoop Connectors

      Importing and Exporting Data from MySQL to HDFS

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  • Apache Sqoop
    • Apache Flume

      Flume Model

      Scalability in Flume

      Components in Flume’s Architecture

      Configuring Flume Components

      Ingest Twitter Data

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  • Getting Data into HDFS
    • Data Ingestion Overview

      Ingesting Data from External Sources with Flume

      Ingesting Data from Relational Databases with Sqoop

      REST Interfaces

      Best Practices for Importing Data

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  • Apache Kafka
    • Apache Kafka

      Aggregating User Activity Using Kafka

      Kafka Data Model


      Apache Kafka Architecture

      Setup Kafka Cluster

      Producer Side API Example

      Consumer Side API

      Consumer Side API Example

      Kafka Connect

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  • Hadoop Clients
    • What is a Hadoop Client

      Installing and Configuring Hadoop Clients

      Installing and Configuring Hue

      Hue Authentication and Authorization

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  • Cluster Maintenance
    • Checking HDFS Status

      Copying Data between Clusters

      Adding and Removing Cluster Nodes

      Rebalancing the Cluster

      Cluster Upgrading

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  • Cloudera Manager
    • The Motivation for Cloudera Manager

      Cloudera Manager Features

      Express and Enterprise Versions

      Cloudera Manager Topology

      Installing Cloudera Manager

      Installing Hadoop Using Cloudera Manager

      Performing Basic Administration Tasks using Cloudera Manager

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  • Cluster Monitoring and Troubleshooting
    • General System Monitoring

      Monitoring Hadoop Clusters

      Common Troubleshooting Hadoop Clusters

      Common Misconfigurations

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  • Planning Your Hadoop Cluster
    • General Planning Considerations

      Choosing the Right Hardware

      Network Considerations

      Configuring Nodes

      Planning for Cluster Management

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  • Advanced Cluster Configuration
    • Advanced Configuration Parameters

      Configuring Hadoop Ports

      Explicitly Including and Excluding Hosts

      Configuring HDFS for Rack Awareness

      Configuring HDFS High Availability

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  • MapReduce Framework
    • What is MapReduce

      Basic MapReduce Concepts

      Distributed Processing in MapReduce

      Word Count Example

      Map Execution Phases

      Map Execution Distributed Two Node Environment

      MapReduce Jobs

      Hadoop MapReduce Job Work Interaction

      Setting Up the Environment for MapReduce Development

      Set of Classes

      Creating a New Project

      Advanced MapReduce

      Data Types in Hadoop

      Output formats in MapReduce

      Using Distributed Cache

      Joins in MapReduce

      Replicated Join

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  • Apache PIG
    • Introduction to Pig

      Components of Pig

      Pig Data Model

      Pig Interactive Modes

      Pig Operations

      Various Relations Performed by Developers

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  • Apache HIVE
    • Introduction to Apache Hive

      Hive SQL over Hadoop MapReduce

      Hive Architecture

      Interfaces to Run Hive Queries

      Running Beeline from Command Line

      Hive Meta Store

      Hive DDL and DML

      Creating New Table

      Data Types

      Validation of Data

      File Format Types

      Data Serialization

      Hive Table and Avro Schema

      Hive Optimization Partitioning Bucketing and Sampling

      Non-Partitioned Table

      Data Insertion

      Dynamic Partitioning in Hive


      What Do Buckets Do

      Hive Analytics UDF and UDAF

      Other Functions of Hive

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  • No SQL Databases HBase
    • NoSQL Databases HBase

      NoSQL Introduction

      HBase Overview

      HBase Architecture

      Data Model

      Connecting to HBase

      HBase Shell

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  • Functional Programming using Scala
    • Basics of Functional Programming and Scala

      Introduction to Scala

      Scala Installation

      Functional Programming

      Programming with Scala

      Basic Literals and Arithmetic Programming

      Logical Operators

      Type Inference Classes Objects and Functions in Scala

      Type Inference Functions Anonymous Function and Class


      Types of Collections

      Operations on List

      Scala REPL

      Features of Scala REPL

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  • Apache Spark
    • Apache Spark Next-Generation Big Data Framework

      History of Spark

      Limitations of MapReduce in Hadoop

      Introduction to Apache Spark

      Components of Spark

      Application of In-memory Processing

      Hadoop Ecosystem vs Spark

      Advantages of Spark

      Spark Architecture

      Spark Cluster in Real World

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  • Data warehouse in Hadoop
    • Hadoop and the Data Warehouse

      Hadoop Differentiators

      Data Warehouse Differentiators

      When and Where to Use Which

  • Augmenting Enterprise Data Warehouse
    • Introduction

      RDBMS Strengths

      RDBMS Weaknesses

      Typical RDBMS Scenario

      OLAP Database Limitations

      Using Hadoop to Augment Existing Databases

      Benefits of Hadoop

      Hadoop Trade-offs

  • Advance Programming in Hadoop
    • Advance Programming in Hadoop

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  • Writing MapReduce Program
    • A Sample MapReduce Program: Introduction

      Map Reduce: List Processing

      MapReduce Data Flow

      The MapReduce Flow: Introduction

      Basic MapReduce API Concepts

      Putting Mapper & Reducer together in MapReduce

      Our MapReduce Program: Word Count

      Getting Data to the Mapper

      Keys and Values are Objects

      What is Writable Comparable

      Writing MapReduce application in Java

      The Driver

      The Driver: Complete Code

      The Driver: Import Statements

      The Driver: Main Code

      The Driver Class: Main Method

      Sanity Checking the Job’s Invocation

      Configuring the Job with Job Conf

      Creating a New Job Conf Object

      Naming the Job

      Specifying Input and Output Directories

      Specifying the Input Format

      Determining Which Files to Read

      Specifying Final Output with Output Format

      Specify the Classes for Mapper and Reducer

      Specify the Intermediate Data Types

      Specify the Final Output Data Types

      Running the Job

      Reprise: Driver Code

      The Mapper

      The Mapper: Complete Code

      The Mapper: import Statements

      The Mapper: Main Code

      The Map Method

      The map Method: Processing the Line

      Reprise: The Map Method

      The Reducer

      The Reducer: Complete Code

      The Reducer: Import Statements

      The Reducer: Main Code

      The reduce Method

      Processing the Values

      Writing the Final Output

      Reprise: The Reduce Method

      Speeding up Hadoop development by using Eclipse

      Integrated Development Environments

      Using Eclipse

      Writing a MapReduce program

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  • Introduction to Combiner
    • The Combiner

      MapReduce Example: Word Count

      Word Count with Combiner

      Specifying a Combiner

      Demonstration: Writing and Implementing a Combiner

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  • Sorting & Searching large data sets
    • Introduction


      Sorting as a Speed Test of Hadoop

      Shuffle and Sort in MapReduce


  • Performing a secondary sort
    • Secondary Sort: Motivation

      Implementing the Secondary Sort

      Secondary Sort: Example

  • Indexing data and inverted Index
    • Indexing

      Inverted Index Algorithm

      Inverted Index: Data Flow

      Aside: Word Count

  • Term Frequency - Inverse Document Frequency (TF- IDF)
    • Term Frequency Inverse Document Frequency (TF-IDF)

      TF-IDF: Motivation

      TF-IDF: Data Mining Example

      TF-IDF Formally Defined

      Computing TF-IDF

  • Calculating Word co- occurrences
    • Word Co-Occurrence: Motivation

      Word Co-Occurrence: Algorithm

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+ More Lessons

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Prepare & Practice for real-life job interviews by joining the Mock Interviews drive at Croma Campus and learn to perform with confidence with our expert team.Not sure of Interview environments? Don’t worry, our team will familiarize you and help you in giving your best shot even under heavy pressures.Our Mock Interviews are conducted by trailblazing industry-experts having years of experience and they will surely help you to improve your chances of getting hired in real.
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Validate your skills and knowledge by working on industry-based projects that includes significant real-time use cases.Gain hands-on expertize in Top IT skills and become industry-ready after completing our project works and assessments.Our projects are perfectly aligned with the modules given in the curriculum and they are picked up based on latest industry standards. Add some meaningful project works in your resume, get noticed by top industries and start earning huge salary lumps right away.
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Big Data is one of the accelerating and most promising fields that is being adopted by organizations of all kinds. Also, you will get huge salary packages.

  • Software Developers
  • Senior IT Professionals
  • Mainframe professionals
  • Data Engineers
  • DBAs and DB professionals
  • Testing professionals
  • Software Architects

There are no such prerequisites for Big Data and Hadoop Course.

    The different payment methods accepted by us are
  • Cash.
  • Debit Card.
  • Credit Card.
  • Net Banking.
  • Cheque.
  • PayPal.
  • Visa.

We help in preparing a resume, assist in interview preparation, real-time projects to students, ensuring they can get placed in a leading MNC and corporate giant.

Career Assistancecareer assistance
  • - Build an Impressive Resume
  • - Get Tips from Trainer to Clear Interviews
  • - Attend Mock-Up Interviews with Experts
  • - Get Interviews & Get Hired
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